'''Trains a simple convnet on the MNIST dataset.

Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''

from __future__ import print_function
import sys
import os
sys.path.insert(0,'/home/xmli/test_code/livertumor/Keras-2.0.8')
sys.path.insert(0,'/home/xmli/test_code/livertumor/mylib')
sys.path.insert(0,'/home/xmli/test_code/livertumor/')
import keras
import numpy as np
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
import matplotlib.pyplot as plt

os.environ['CUDA_VISIBLE_DEVICES'] = '0'
batch_size = 128
num_classes = 10
epochs = 12

# input image dimensions
img_rows, img_cols = 28, 28

# the data, shuffled and split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

img = x_train[7,:,:,0]
img_rotate = np.rot90(img, k = 3)
# plt.figure(1)
# plt.imshow(img,'gray')
# plt.figure(2)
# plt.imshow(img_rotate,'gray')
# # plt.show()



# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Dropout(0.25))
# model.add(Flatten())
# model.add(Dense(128, activation='relu'))
# model.add(Dropout(0.5))
# model.add(Dense(num_classes, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=['accuracy'])

# model.fit(x_train, y_train,
#           batch_size=batch_size,
#           epochs=epochs,
#           verbose=1,
#           validation_data=(x_test, y_test))
model.load_weights('mymodel.h5', by_name=True)
img_test = np.zeros((1,28,28,1),dtype='float32')
img_test[0,:,:,0] = img_rotate
score = model.predict(img_test)
score = score[0,:,:,5]
plt.figure(1)
plt.imshow(score,'gray')
# plt.figure(2)
# plt.imshow(img_rotate,'gray')
plt.show()
exit(0)

# score = model.evaluate(x_test, y_test, verbose=0)
# print('Test loss:', score[0])
# print('Test accuracy:', score[1])
